Wear detection in mechanical equipment

2. The method of claim 1, wherein the recommendation is automatically generated by a machine learning process that takes the wear pattern as input along with monitoring data for the piece of equipment.

3. The method of claim 2, wherein the monitoring data comprises an analysis of lubrication used with the equipment.

4. The method of claim 2, wherein the machine learning process is an association rule engine.

5. The method of claim 1, wherein the machine classifier is a convolutional neural network.

6. The method of claim 1, wherein the wear pattern indicates wear selected from the group consisting of pitting, spalling, abrasive wear, corrosive wear, machine marks, honing marks, and thermal stress.

7. The method of claim 1, wherein the portion is a gear tooth.